Machine learning models based methods and systems for determining prospective acquisitions between business entities

ABSTRACT

Embodiments provide methods and systems for determining prospective acquisitions among business entities using machine learning techniques. Method performed by server system includes accessing financial data items and news items associated with finances of business entities from data sources for particular time duration. Method includes generating financial and news feature vectors corresponding to business entities and applying machine learning models over financial feature vectors and news feature vectors associated with business entities for determining candidate set of business entities predicted to be engaged in business acquisition in future. Method includes creating dynamic bipartite knowledge graph for each distinct time durations within particular time duration and generating static bipartite knowledge graph based on dynamic bipartite knowledge graphs for distinct time durations. Method includes predicting occurrence of acquisition of at least one business entity of candidate set of business entities based on supervised machine learning model and static bipartite knowledge graph.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application claims priority to India Patent Application No.202141016079 filed Apr. 5, 2021, entitled “Machine Learning Models BasedMethods and Systems for Determining Prospective Acquisitions BetweenBusiness Entities”, the entirety of which is incorporated herein byreference.

TECHNICAL FIELD

The present disclosure relates to artificial intelligence processingsystems and, more particularly to, electronic methods and complexprocessing systems for determining prospective business acquisitionsamong business entities, through use of machine learning techniques.

BACKGROUND

Nowadays mergers and tie-ups have become crucial business decisions invarious business entities. Before taking investment decisions, businessentities determine how combining features with another business entitywill affect their overall growth. This is necessary to avoid any lossesor collapse in market value. Therefore, predicting an overall growthafter acquiring or after being acquired by another business entity isnecessary for positive market growth.

This prediction of overall growth is performed by taking into accountexperience and prior knowledge of the businesses. Therefore, theprediction is usually performed by humans. However, there are variousshortcomings in predicting acquisitions and growth rates afteracquisitions by humans. Firstly, it is prone to errors as a lot of datais involved. Any miss in crucial information may result in wrongpredictions. Further, it is risky to make such crucial decisions onbleak guess work. In addition, since the prediction is performed byhuman, limited business entities and dimensions can be taken intoaccount while considering acquisitions.

Therefore, there exists a need to address the above-mentioned problemsof relying on humans as agents to predict the acquisitions and thegrowth rate. More particularly, there is a technological need toautomate the process of predicting acquisitions and determining growthrates of business entities after acquisitions.

SUMMARY

Various embodiments of the present disclosure provide methods and systemfor determining prospective acquisitions among business entities usingmachine learning techniques.

In an embodiment, a computer-implemented method is disclosed. Thecomputer-implemented method performed by a server system includesaccessing financial data items and news items associated with financesof a plurality of business entities from one or more data sources for aparticular time duration. The computer-implemented method furtherincludes generating a plurality of financial feature vectors and aplurality of news feature vectors corresponding to the plurality ofbusiness entities based, at least in part, on the financial data itemsand news items associated with the finances. The computer-implementedmethod also includes applying machine learning models over the pluralityof financial feature vectors and the plurality of news feature vectorsassociated with the plurality of business entities for determining acandidate set of business entities predicted to be engaged in businessacquisition in future. The computer-implemented method includes creatinga dynamic bipartite knowledge graph for each of distinct time durationswithin the particular time duration based, at least in part, oncompany-specific features of the candidate set of business entities. Thedynamic bipartite knowledge graph represents a computer-based graphrepresentation of the candidate set of business entities as nodes, andrelationship between the nodes as edges. Further, thecomputer-implemented method includes generating a static bipartiteknowledge graph based, at least in part, on dynamic bipartite knowledgegraphs created for the distinct time durations. Nodes of the staticbipartite knowledge graph represent the candidate set of businessentities and a pre-determined time duration and edges of the staticbipartite knowledge graph represent relationship between the nodes forthe pre-determined time duration. The computer-implemented method alsoincludes predicting an occurrence of an acquisition of at least onebusiness entity of the candidate set of business entities based, atleast in part, on a supervised machine learning model and the staticbipartite knowledge graph.

In another embodiment, a server system is disclosed. The server systemincludes a communication interface, a memory including executableinstructions, and a processor communicably coupled to the communicationinterface. The processor is configured to execute the executableinstructions to cause the server system to at least access financialdata items and news items associated with finances of a plurality ofbusiness entities from one or more data sources for a particular timeduration. The server system is further caused to generate a plurality offinancial feature vectors and a plurality of news feature vectorscorresponding to the plurality of business entities based, at least inpart, on the financial data items and news items associated with thefinances. The server system is also caused to apply machine learningmodels over the plurality of financial feature vectors and the pluralityof news feature vectors associated with the plurality of businessentities for determining a candidate set of business entities predictedto be engaged in business acquisition in future. Further, the serversystem is caused to create a dynamic bipartite knowledge graph for eachof distinct time durations within the particular time duration based, atleast in part, on company-specific features of the candidate set ofbusiness entities. The dynamic bipartite knowledge graph represents acomputer-based graph representation of the candidate set of businessentities as nodes, and relationship between the nodes as edges.Furthermore, the server system is caused to generate a static bipartiteknowledge graph based, at least in part, on dynamic bipartite knowledgegraphs created for the distinct time durations. Nodes of the staticbipartite knowledge graph represent the candidate set of businessentities and a pre-determined time duration and edges of the staticbipartite knowledge graph represent relationship between the nodes forthe pre-determined time duration. Further, the server system is causedto predict an occurrence of an acquisition of at least one businessentity of the candidate set of business entities based, at least inpart, on the static bipartite knowledge graph and a supervised machinelearning model.

In another embodiment, a computer-implemented method is disclosed. Thecomputer-implemented method performed by a server system includesaccessing financial data items and news items associated with financesof a plurality of business entities from one or more data sources for aparticular time duration. The computer-implemented method furtherincludes generating a plurality of financial feature vectors and aplurality of news feature vectors corresponding to the plurality ofbusiness entities based, at least in part, on the financial data itemsand news items associated with the finances. The computer-implementedmethod also includes applying machine learning models over the pluralityof financial feature vectors and the plurality of news feature vectorsassociated with the plurality of business entities for determining acandidate set of business entities predicted to be engaged in businessacquisition in future. The computer-implemented method includes creatinga dynamic bipartite knowledge graph for each of distinct time durationswithin the particular time duration based, at least in part, oncompany-specific features of the candidate set of business entities. Thedynamic bipartite knowledge graph represents a computer-based graphrepresentation of the candidate set of business entities as nodes, andrelationship between the nodes as edges. Further, thecomputer-implemented method includes generating a static bipartiteknowledge graph based, at least in part, on dynamic bipartite knowledgegraphs created for the distinct time durations. Nodes of the staticbipartite knowledge graph represent the candidate set of businessentities and a pre-determined time duration and edges of the staticbipartite knowledge graph represent relationship between the nodes forthe pre-determined time duration. Additionally, the computer-implementedmethod includes encoding the static bipartite knowledge graph into graphembedding vectors based, at least in part, on a bipartite graphembedding model. The computer-implemented method also includespredicting an occurrence of an acquisition of at least one businessentity of the candidate set of business entities by applying asupervised machine learning model over the graph embedding vectors.

BRIEF DESCRIPTION OF THE FIGURES

For a more complete understanding of example embodiments of the presenttechnology, reference is now made to the following descriptions taken inconnection with the accompanying drawings in which:

FIG. 1 is an example representation of an environment, related to atleast some example embodiments of the present disclosure;

FIG. 2 is a simplified block diagram of a server system, in accordancewith one embodiment of the present disclosure;

FIG. 3. is an example representation of financial data which are used tocreate financial feature vectors, in accordance with one embodiment ofthe present disclosure;

FIG. 4 is a schematic block diagram depicting creation of news featurevectors, in accordance with one embodiment of the present disclosure;

FIG. 5 is a schematic block diagram of a candidate generation engine, inaccordance with one embodiment of the present disclosure;

FIG. 6 is a schematic block diagram representation conversion of dynamicbipartite knowledge graph into static bipartite knowledge graph, inaccordance with one embodiment of the present disclosure; and

FIG. 7 represents a flow diagram of a method for predicting futureacquisitions, in accordance with one embodiment of the presentdisclosure.

The drawings referred to in this description are not to be understood asbeing drawn to scale except if specifically noted, and such drawings areonly exemplary in nature.

DETAILED DESCRIPTION

In the following description, for purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present disclosure. It will be apparent, however,to one skilled in the art that the present disclosure can be practicedwithout these specific details.

Reference in this specification to “one embodiment” or “an embodiment”means that a particular feature, structure, or characteristic describedin connection with the embodiment is included in at least one embodimentof the present disclosure. The appearance of the phrase “in anembodiment” in various places in the specification is not necessarilyall referring to the same embodiment, nor are separate or alternativeembodiments mutually exclusive of other embodiments. Moreover, variousfeatures are described which may be exhibited by some embodiments andnot by others. Similarly, various requirements are described which maybe requirements for some embodiments but not for other embodiments.

The terms “business entities”, “companies”, or “financial institution”herein refer to any financial enterprises, financial companies, ororganizations of financial domain.

The term “news sources” refers to news-affiliated entity or an onlinenews provider providing news content. For example, a news source may bea news broadcasting company or a newspaper company. The news source maybe associated with a web site having various web pages, a televisionprogram, an online or printed newspaper, an online journal, an onlineblog, or other information-delivery medium. Examples of news sourcesinclude news broadcasting companies, news web sites, newspaperpublishers and/or and their affiliated web sites. The web pagesassociated with a news source may include news stories, articles, shortnews features (summaries, excerpts, etc.), photos, live video streaming,audio packages, commentaries, blogs, interactive multimedia, searchablearchives of news features, background information, and the like.

The term “graph embedding vector” refers to include a set of measurableproperties (or “features”) that represent some objects or relationbetween two entities. A graph embedding vector can include collectionsof data represented digitally in an array or vector structure. A graphembedding vector may also include collections of data that can berepresented as a mathematical vector, on which vector operations such asthe scalar product can be performed. A graph embedding vector can bedetermined or generated from a knowledge graph after graph traversal. Agraph embedding vector can be used as the input to a machine learningmodel, such that the machine learning model produces some output orclassification.

Moreover, although the following description contains many specifics forthe purposes of illustration, anyone skilled in the art will appreciatethat many variations and/or alterations to said details are within thescope of the present disclosure. Similarly, although many of thefeatures of the present disclosure are described in terms of each other,or in conjunction with each other, one skilled in the art willappreciate that many of these features can be provided independently ofother features. Accordingly, this description of the present disclosureis set forth without any loss of generality to, and without imposinglimitations upon, the present disclosure.

Overview

Various example embodiments of the present disclosure provide methods,and server system for determining prospective acquisitions betweenbusiness entities using machine learning techniques. The prediction ofacquisitions is based on financial data of the business entities. Theacquisitions of business entities are also based on the real-time newsrelated to the business entities collected from various news sources.

In various example embodiments, the present disclosure describes aserver system that predicts acquisitions of business entities and theirgrowth rates after acquisitions. The server system is configured toaccess financial data items and news items of a plurality of businessentities. The financial data items corresponding to a business entitymay include, but are not limited to, total assets, asset growth rate,total loans, total losses, loan growth rate, gross loans, gross assets,total deposits, etc. The news items of a business entity may befinancial news related to the business entity gathered from a variety ofnews sources. The news items may be, but not limited to, financial news,or management news. The news items may also be gathered from blogs,articles, newspapers, news channels, internet, social media websites,websites of various business entities etc.

Subsequent to accessing the financial data items and news items, theserver system is configured to generate a plurality of financial featurevectors corresponding to the plurality of business entities based atleast in part on financial data items. The server system is configuredto generate a plurality of news feature vectors corresponding to theplurality of business entities based at least in part on the news items.The server system is configured to apply machine learning models overthe plurality of financial feature vectors and the plurality of newsfeature vectors associated with the plurality of business entities fordetermining a candidate set of business entities predicted to be engagedin business acquisition in future.

In particular, the server system is configured to apply a first longshort-term memory (LSTM) model using the plurality of financial featurevectors to output a first set of vectors. The server system is furtherconfigured to apply a second LSTM model using the plurality of newsfeature vectors to output a second set of vectors. The server system isconfigured to concatenate the first set of vectors and the second set ofvectors to obtain a set of concatenated vectors. For each businessentity of the plurality of business entities, the server system isconfigured to generate a probable growth rate on acquiring otherbusiness entity and a probable growth rate on getting acquired by otherbusiness entity by applying a feed forward neural network model over theset of concatenated vectors. The server system is then configured toselect the candidate set of business entities predicted to be engaged inbusiness acquisition in future based on the generated probable growthrates. The server system is configured to select those business entitieswhich have high probable growth on acquiring other business entities andto select those business entities which have high probable growth rateon getting acquired by other business entities.

Thereafter, the server system is configured to create a dynamicbipartite knowledge graph for each of distinct time durations (e.g.,“quarter of a year”) within the particular time duration based, at leastin part, on company-specific features of the candidate set of businessentities. The company-specific features may include the financial dataitems, the news items, and the generated probable growth ratesassociated with each business entity. Each node of the dynamic bipartiteknowledge graph corresponds to each business entity of the candidate setof business entities. Each edge of the dynamic bipartite knowledge graphrepresents acquisition of a business entity of the candidate set ofbusiness entities. The strength of each edge in the dynamic bipartiteknowledge graph represents the probable growth rates generated by theserver system while determining the candidate set of business entities.

In one embodiment, the server system is configured to generate a staticbipartite knowledge graph based, at least in part, on the dynamicbipartite knowledge graph. Each node of the static bipartite knowledgegraph represents features of each business entity of the candidate setof business entities for a pre-determined time duration. The serversystem if also configured to convert the static bipartite knowledgegraph into positive and negative triplets. The positive triplets are(N_(p1), N_(p2), E_(p1)). N_(p1) represents a first business entity,N_(p2) represents a second business entity, and E_(p1) represents analready existing link between the first and second business entities.The negative triplets are (N_(n1), N_(n2), E_(n1)). N_(n1) represents afirst business entity, N_(n2) represents a second business entity, andE_(n1) represents no link present between the first and second businessentities. The server system is also configured to encode the staticbipartite knowledge graph into graph embedding vectors based at least ona bipartite graph embedding model and apply a supervised machinelearning model over the graph embedding vectors to predict an occurrenceof an acquisition of at least one business entity of the candidate setof business entities.

Various embodiments of the present disclosure offer multiple advantagesand technical effects. For instance, the present disclosure provides asystem for predicting acquisitions or mergers by taking into account ahuge amount of data of a large number of business entities. This makesthe prediction error free and reliable. Further, no crucial informationis missed since the prediction is performed by a machine learning modelrather than by a human. Further, at the same time multiple predictionsof acquisitions are possible.

Various example embodiments of the present disclosure are describedhereinafter with reference to FIGS. 1 to 7.

FIG. 1 illustrates an exemplary representation of an environment 100related to at least some example embodiments of the present disclosure.Although the environment 100 is presented in one arrangement, otherembodiments may include the parts of the environment 100 (or otherparts) arranged otherwise depending on, for example, predictingacquisitions, etc. The environment 100 generally includes a plurality ofbusiness entities 102 a, 102 b . . . 102 n, a server system 104, and oneor more news sources 106 a, 106 b . . . 106 n, each coupled to, and incommunication with (and/or with access to) a network 108. The network108 may include, without limitation, a light fidelity (Li-Fi) network, alocal area network (LAN), a wide area network (WAN), a metropolitan areanetwork (MAN), a satellite network, the Internet, a fiber optic network,a coaxial cable network, an infrared (IR) network, a radio frequency(RF) network, a virtual network, and/or another suitable public and/orprivate network capable of supporting communication among two or more ofthe entities illustrated in FIG. 1, or any combination thereof.

Various entities in the environment 100 may connect to the network 108in accordance with various wired and wireless communication protocols,such as Transmission Control Protocol and Internet Protocol (TCP/IP),User Datagram Protocol (UDP), 2^(nd) Generation (2G), 3^(rd) Generation(3G), 4^(th) Generation (4G), 5^(th) Generation (5G) communicationprotocols, Long Term Evolution (LTE) communication protocols, or anycombination thereof. For example, the network 108 may include multipledifferent networks, such as a private network made accessible by theplurality of business entities 102 a, 102 b . . . 102 n, and one or morenews sources 106 a, 106 b . . . 106 n, separately, and a public network(e.g., the Internet etc.) through which the plurality of serversassociated with the plurality of business entities 102 a, 102 b . . .102 n, and the one or more news sources 106 a, 106 b . . . 106 n, andthe server system 104 may communicate. The plurality of businessentities 102 a, 102 b . . . 102 n hereinafter is collectivelyrepresented as “the plurality of business entities 102” or “businessentities 102”. The plurality of news sources 106 a, 106 b . . . 106 nhereinafter is collectively represented as “the news sources 106”.

In one example, the plurality of business entities 102 may be, but notlimited to, financial enterprises, financial companies, or organizationsof financial domain. The news sources 106 refer to servers of any newschannel dealing with financial news or business news, or any blogs,articles available on the Internet.

The server system 104 includes a processor and a memory. The serversystem 104 is configured to perform one or more of the operationsdescribed herein. In general, the server system 104 is configured topredict at least one business acquisition in future. The server system104 should be understood to be embodied in at least one computing devicein communication with the network 108, which may be specificallyconfigured, via executable instructions, to perform as described herein,and/or embodied in at least one non-transitory computer readable media.The environment 100 further shows a database 110 which is accessible bythe server system 104. In another embodiment, the database 110 isincorporated in the server system 104.

The server system 104 is configured to predict business acquisitions ofat least one business entity in future. The server system 104 isconfigured to collect news items from the news sources 106 and financialdata from the business entities 102. In an alternate embodiment, theserver system 104 is configured to store the news items and financialdata in the database 110 and then access the relevant data from thedatabase 110 when required. Using the collected news items and financialdata, the server system 104 is configured to generate a candidate set ofbusiness entities which may be engaged in business acquisitions infuture. The server system 104 is configured to generate knowledge graphsbased on the candidate set of business entities and finally, the serversystem 104 is configured to predict the links between the candidate setof business entities based on the knowledge graphs. These predictedlinks represent future acquisition of business entities.

The number and arrangement of systems, devices, and/or networks shown inFIG. 1 are provided as an example. There may be additional systems,devices, and/or networks; fewer systems, devices, and/or networks;different systems, devices, and/or networks; and/or differently arrangedsystems, devices, and/or networks than those shown in FIG. 1.Furthermore, two or more systems or devices shown in FIG. 1 may beimplemented within a single system or device, or a single system ordevice shown in FIG. 1 may be implemented as multiple, distributedsystems or devices. Additionally, or alternatively, a set of systems(e.g., one or more systems) or a set of devices (e.g., one or moredevices) of the environment 100 may perform one or more functionsdescribed as being performed by another set of systems or another set ofdevices of the environment 100.

Referring now to FIG. 2, a simplified block diagram of a server system200, is shown, in accordance with an embodiment of the presentdisclosure. The server system 200 is similar to the server system 104.In some embodiments, the server system 200 is embodied as a cloud-basedand/or SaaS-based (software as a service) architecture. The serversystem 200 includes a computer system 202 and a database 204. Thecomputer system 202 includes at least one processor 206 for executinginstructions, a memory 208, and a communication interface 210, thatcommunicate with each other via a bus 214.

In some embodiments, the database 204 is integrated within the computersystem 202. For example, the computer system 202 may include one or morehard disk drives as the database 204. A storage interface 212 is anycomponent capable of providing the processor 206 with access to thedatabase 204. The storage interface 212 may include, for example, anAdvanced Technology Attachment (ATA) adapter, a Serial ATA (SATA)adapter, a Small Computer System Interface (SCSI) adapter, a RAIDcontroller, a SAN adapter, a network adapter, and/or any componentproviding the processor 206 with access to the database 204.

In one embodiment, the database 204 is configured to store one or moretrained machine learning models for determining a candidate set ofbusiness entities which may be engaged in business acquisition infuture.

The processor 206 includes a data pre-processing engine 216, a candidategeneration engine 218, a knowledge graph generation engine 220, a graphembedding engine 222, and a prediction engine 224.

The processor 206 includes suitable logic, circuitry, and/or interfacesto execute operations for receiving financial data items and news itemsassociated with finances (also referred as “financial news items”) ofthe plurality of business entities 102. Examples of the processor 206include, but are not limited to, an application-specific integratedcircuit (ASIC) processor, a reduced instruction set computing (RISC)processor, a complex instruction set computing (CISC) processor, afield-programmable gate array (FPGA), and the like. The memory 208includes suitable logic, circuitry, and/or interfaces to store a set ofcomputer-readable instructions for performing operations. Examples ofthe memory 208 include a random-access memory (RAM), a read-only memory(ROM), a removable storage drive, a hard disk drive (HDD), and the like.It will be apparent to a person skilled in the art that the scope of thedisclosure is not limited to realizing the memory 208 in the serversystem 200, as described herein. In another embodiment, the memory 208may be realized in the form of a database server or cloud storageworking in conjunction with the server system 200, without departingfrom the scope of the present disclosure.

The processor 206 is operatively coupled to the communication interface210 such that the processor 206 is capable of communicating with aremote device 226 such as, server associated with business entities 102or news sources 106 or communicated with any entity connected to thenetwork 108 (as shown in FIG. 1).

It is noted that the server system 200 as illustrated and hereinafterdescribed is merely illustrative of an apparatus that could benefit fromembodiments of the present disclosure and, therefore, should not betaken to limit the scope of the present disclosure. It is noted that theserver system 200 may include fewer or more components than thosedepicted in FIG. 2.

The data pre-processing engine 216 includes suitable logic and/orinterfaces for accessing financial data items and news items associatedwith finances (also referred as “financial news items”) of the pluralityof business entities 102 (i.e., “financial companies”) from one or moredata sources (e.g., “S&P financial blog”, news sources 106, etc.) for aparticular time duration (e.g., one year). The data-preprocessing engine216 is configured to perform data-cleaning, normalization, and featureextraction, etc. In one embodiment, the data pre-processing engine 216is configured to analyze the financial data items and the news items andextract predictive features associated with the plurality of businessentities 102. The financial data items of a particular business entitymay include, but not limited to, total assets, asset growth rate, totalloans, total losses, loan growth rate, gross loans, gross assets, andtotal deposits, etc. The financial data items may be collected fromvarious sources like servers associated with business entities, revenuereports associated with business entities, etc. Further, the financialnews items associated with a particular business entity may include, butare not limited to, financial news, management news, blogs, and articlesrelated to finance and business.

The financial data items and the financial news items associated withthe plurality of business entities are collected for a pre-determinedtime duration. In an exemplary embodiment, the financial data items andthe financial news items of the past 8 quarters of the last 2 years arecollected. Further, the financial data items and the financial newsitems associated with the plurality of business entities arecontinuously updated.

In one embodiment, the data pre-processing engine 216 may use naturallanguage processing (NLP) algorithms to generate a plurality ofmulti-dimensional financial feature vectors and a plurality ofmulti-dimensional news feature vectors from the financial data items andthe news items, respectively. The financial data items are shown indetail in FIG. 3. Further, the process of generating news featurevectors from the financial news items is shown in FIG. 4.

In the training phase, the data pre-processing engine 216 is configuredto access the financial data items and the financial news itemsassociated with a plurality of business entities for a longer timeduration, for example, the first 6 quarters of past 2 years. However,for execution phase or prediction phase, the data pre-processing engine216 may be configured to access the financial data items and thefinancial news items associated with the plurality of business entitiesfor a time duration shorter than that used in the training phase, forexample, last 2 quarters of last year.

The candidate generation engine 218 includes suitable logic and/orinterfaces for determining a candidate set of business entitiespredicted to be engaged in business acquisition in the future usingmachine learning models. The candidate generation engine 218 isconfigured to apply various machine learning models over the pluralityof financial feature vectors and the plurality of news feature vectors.The process of generation of the candidate set of business entities bythe candidate generation engine 218 is shown in FIG. 5 and is explainedin detail later.

In one embodiment, the candidate generation engine 218 is configured topredict future values associated with each financial data items. Inparticular, the candidate generation engine 218 is configured to outputthe first set of vectors based, at least in part, on the plurality offinancial feature vectors using a first long short-term memory (LSTM)machine learning model. In particular, the candidate generation engine218 is configured to apply the first LSTM model over the plurality offinancial feature vectors to output the first set of vectors.

In general, LSTMs are specializations of Recurrent Neural Networks(RNN), which have the form of a chain of repeating neural networkmodules. The RNNs have emerged as the preferred neural networkarchitecture for data that is modeled as a sequence. In the context ofthe financial features of a business entity, the first LSTM machinelearning model is configured to predict financial features (such as,assets, growth rate, loan, market share value, etc.). The LSTM model istrained to learn long-term dependencies of each financial features.During the training, weights and biases associated with the LSTM modelare computed by minimizing an objective function.

In particular, the first LSTM model is trained based on past financialdata items of the plurality of business entities 102 for a long timeduration. During the execution, the first LSTM model is able to predictfuture (e.g., “next month quarter”) values associated with the pluralityof financial features of the plurality of business entities 102.

In similar manner, the news features are fed to a second LSTM model forpredicting second output vectors. The second LSTM model is applied overthe plurality of news feature vectors to output a second set of vectors.

The candidate generation engine 218 is configured to combine the firstset of vectors and the second set of vectors and provide the combinedvectors to a feed-forward neural network model for generating a probablegrowth rate on getting acquired and a probable growth rate on acquiringfor each business entity. The feed-forward neural network modelincludes, but is not limited to, multiple LSTM layers, and dense layers.The feed-forward neural network model uses the number of businessentities, the number of quarters, and the number of financial featuresas input features for the feed-forward neural network model. In oneembodiment, after training the feed-forward neural network model, thefeed-forward neural network model is configured to determine a candidateset of business entities which may engaged in acquisition in futurebased on the probable growth rates, thereby reducing datasets forfurther processing.

The knowledge graph generation engine 220 includes suitable logic and/orinterfaces for creating a dynamic bipartite knowledge graph for each ofdistinct time durations (e.g., “quarter of a year”) within theparticular time duration (e.g., 1 year) based on company-specificfeatures (including the plurality of financial features, the pluralityof news features, and the probable growth rates of getting acquired andacquiring) of the candidate set of business entities. Initially, thedynamic bipartite knowledge graph is configured using the probablegrowth rates associated with the candidate set of business entities. Thedynamic bipartite knowledge graph includes multiple sub-graphs for thedistinct time durations. For example, each sub-graph may correspond to aquarter of a year. Therefore, the dynamic bipartite knowledge graph fora year will have four sub-graphs. Each sub-graph corresponds to eachquarter of the year.

Further, each node in a sub-graph corresponds to each business entity ofthe candidate set of business entities. Each node in the dynamicbipartite knowledge graph represents the financial features of abusiness entity. The dynamic bipartite knowledge graph is a directedgraph, and it gets modified with time. Each edge joining two nodes inthe sub-graphs represents a relationship between the business entitiescorresponding to the two nodes. The strength of the edge representsgrowth percentage predicted by the candidate generation engine 218. Inone example, an edge represents the existing acquisition relationshipbetween two business entities. For example, an edge directed from afirst node to a second node in a sub-graph of a particular quarterimplies that the business entity associated with the first node hasacquired the business entity associated with the second node in thatparticular quarter.

The knowledge graph generation engine 220 further includes suitablelogic and/or interfaces for creating a static bipartite knowledge graphbased, at least in part, on dynamic bipartite knowledge graphs createdfor the distinct time durations. In one embodiment, the knowledge graphgeneration engine 220 is configured to partition the dynamic bipartiteknowledge sub-graphs seasonally on a timely basis (i.e., on quarterlybasis). The static bipartite knowledge graph includes a plurality ofnodes and edges. Each node in the static bipartite knowledge graphrepresents the financial features of a business entity at a particularquarter. In contrast to the dynamic bipartite knowledge graph, there areno sub-graphs for a particular quarter present in the static bipartiteknowledge graph. Further, the values and features of nodes in the staticbipartite knowledge graph remain fixed as opposed to the dynamicbipartite knowledge graph. Each edge between two nodes represents theacquisition between the two business entities representing the twonodes. The conversion of the dynamic bipartite knowledge graph to thestatic bipartite knowledge graph is explained in detail later withreference to FIG. 6.

The graph embedding engine 222 further includes suitable logic and/orinterfaces for encoding the static bipartite knowledge graph into graphembedding vectors using a bipartite graph embedding model. Moreparticularly, the bipartite graph embedding model may transform thestatic bipartite knowledge graphs into corresponding vectorrepresentations. In general, the bipartite graph embedding modelconverts graph data into a low dimensional space in which graphstructural information and graph properties are preserved at most.

In one embodiment, the bipartite graph embedding model may be determinedby applying sampling, mapping, and optimization processes on the staticbipartite knowledge graph. In one example, the bipartite graph embeddingmodel uses a translation-based model (e.g., TransE model) which isenergy-based model for learning low-dimensional embeddings of entities.In the sampling process, triplets (e.g., two nodes and a relationbetween them) are extracted. Two types of triplets areextracted—positive triplets and negative triplets. The positive tripletsare (N₁, N₂, E₁), where N₁ represents a first business entity, N₂represents a second business entity, and E₁ represents if there isalready a link between the first and second business entities. Thenegative triplets are (N₁, N₂, E₂), N₁, represents a first businessentity, N₂ represents a second business entity, and E₂ represents ifthere is no link between the first and second business entities. In themapping process, embedding stacking operations (e.g., pooling,averaging, etc.) are applied on the triplets. In the optimizationprocess, a set of optimization functions are applied to find a graphembedding model that preserves original properties of the staticbipartite knowledge graph. The set of optimization functions may be, butnot limited to, root mean squared error (RMSE), Log likelihood, etc.During training phase, the bipartite graph embedding model is trained tominimize cost function.

In some embodiments, the bipartite graph embedding model may implementalgorithms (such as, for example, Deepwalk, Matrix factorization,Large-scale information network embedding (LINE), Bayesian personalizedranking, graphlet algorithms, Node2vec., Graph2vec., Structural DeepNetwork Embedding (SDNE) etc.) over the static bipartite knowledgegraph. In one embodiment, the bipartite graph embedding model mayinclude first and second graph embedding models. In particular, thebipartite graph embedding model is utilized for converting the staticbipartite knowledge graph into a latent space representation.

In one example, the bipartite graph embedding model implemented usingDeepwalk models is configured to perform graph traversals from one nodeto another taking direction of the edges into a consideration andaggregate vector representations of traversed nodes next to each otherin a matrix. The vector representations of the nodes are generated basedon the company-specific features. Then, the matrix is provided to arecurrent neural network to generate graph embedding vectors.

During the training phase, the bipartite graph embedding modell learns amapping from a graph network to a vector space, while preservingrelevant graph network properties. The first graph embedding model isthe one in which edges in triplets represents whether a business entitywas acquired by another business entity. The second graph embeddingmodel is the one in which edges in triplets represents whether abusiness entity acquired another business entity.

The prediction engine 224 is configured to predict an occurrence of anacquisition of at least one business entity of the candidate set ofbusiness entities based, at least on, applying a supervised machinelearning model over the graph embedding vectors. In one example, thesupervised machine learning model is the supervised bipartite graph linkprediction model. More particularly, the prediction engine 224 isconfigured to predict whether there will be links between two nodesbased on company-specific feature information associated with the nodesand the observed existing link information.

In one embodiment, the supervised bipartite graph link prediction modelmay be, supervised techniques, such as those involving artificial neuralnetworks, association rule learning, recurrent neural networks (RNN),Bayesian networks, clustering, deep learning, decision trees, geneticalgorithms, Hidden Markov Modeling, inductive logic programming,learning automata, learning classifier systems, logistic regressions,linear classifiers, quadratic classifiers, reinforcement learning,representation learning, rule-based machine learning, similarity andmetric learning, sparse dictionary learning, support vector machines,and/or the like.

In one example, the supervised bipartite link prediction model canidentify promising links in the two different nodes of the dynamicbipartite knowledge graph using the graph embedding vectors. Morespecifically, for each directional edge, the link prediction model canbuild a classifier that outputs the occurrence of a given edge for thatparticular node. An edge representing a business acquisitionrelationship between two companies can be associated with a respectivescore that is calculated by the classifier. The respective score can bebased on a context of a link in a neighborhood of nodes and trendsidentified in the company-specific features of one or more neighborhoodnodes. When the respective score for the edge is greater or equal to apredefined threshold value, the prediction engine 224 notifies marketanalysts about the most probable business acquisition between twobusiness entities associated with the edge occurring within theparticular time duration (e.g., next quarter) in future.

In one embodiment, the supervised bipartite link prediction model istrained by consuming the graph embedding vectors generated at distincttime durations (i.e., last 3 quarters). Then, the supervised bipartitelink prediction model may successfully predict next businessacquisitions among the candidate set of business entities in future whenpreset conditions are met.

Referring now to FIG. 3, an example representation of financial datawhich are used to create financial feature vectors is shown, inaccordance with one embodiment of the present disclosure. Table 300shows the financial data for a business entity named “XYZ” that isaccessed from a server associated with the business entity “XYZ”.Further, for every quarter of a pre-determined time duration, financialdata like total assets, asset growth rate, total loans and losses, loangrowth rate, gross loans and assets, and total deposits are accessed.For example, as shown in the table 300, the financial data for the year2017 and the year 2018 divided into quarters is accessed. Similarly, thefinancial data for the plurality of business entities 102 is accessed bythe data pre-processing engine 216 and it converts this data intofeature vectors for each quarter.

Further, the financial data from the past quarters is used to train themachine learning models, while in the execution phase, financial datafrom recent quarters is used. For example, for training purposes thefinancial data of last two years may be used, while for prediction, thefinancial data of last two quarters may be taken into consideration. Thefinancial data is then converted into financial feature vector by wordto vector encoder present in the data pre-processing engine 216 usingnatural language processing (NLP) techniques.

Referring now to FIG. 4, a schematic block diagram 400 depictingcreation of news feature vectors is shown, in accordance with oneembodiment of the present disclosure. As shown in FIG. 4, thequarter-wise news data or news items for each business entity areaccessed and are fed to one-hot vector encoder 402. It is to be notedthat one-hot vector encoder 402 is included in the data pre-processingengine 216. The one-hot vector encoder 402 converts the quarter-wisenews data into one-hot vector of a vocabulary of words stored in thedatabase 204. For example, if the vocabulary has 1000 words, and thequarter-wise news data for a business entity has 400 words, in that casethe one-hot vector will have 400 ones corresponding to the words presentin the news data and 600 zeroes.

The one-hot vector is then fed to a word to vector encoder 404, whichconverts the one-hot vector into news feature vector which is a 2-Dvector. It is to be noted that word to vector encoder 404 is included inthe data pre-processing engine 216. In one example, the word to vectorencoder 404 utilizes natural language processing (NLP) techniques forcreating the 2-D news feature vectors.

Referring now to FIG. 5, a schematic block diagram of a candidategeneration engine 500 is shown, in accordance with one embodiment of thepresent disclosure. In an embodiment, the candidate generation engine500 is the candidate generation engine 218 as shown in FIG. 2. Thecandidate generation engine 500 includes a long short-term memory (LSTM)module 502, a concatenation module 504, a feed forward neural networkmodule 506, and a selection module 508.

The LSTM module 502 has multiple LSTM models. A first LSTM model is fedwith the financial feature vector to output a first output vector. It isto be noted that the financial feature vector is a 2-dimensional (2-D)vector. A second LSTM model is fed with the news feature vector tooutput a second output vector.

The concatenation module 504 is configured to concatenate the first andsecond output vectors from the LSTM module 502 to output a concatenatedvector.

The feed forward neural network module 506 includes a feed forwardneural network model which is configured to calculate, for everybusiness entity, a growth rate on getting acquired and a growth rate onacquiring. The feed forward neural network model is configured tocalculate the growth rates using the concatenated vector. In the feedforward neural network, the connections between nodes do not form acycle. There are no cycles or loops. Input in the feed forward neuralnetwork model moves in only one direction. In an embodiment, the inputof the feed-forward neural network model is number of business entities,number of quarters, and number of financial and news features. Theoutput of the feed-forward neural network model is number of businessentities and label corresponding to the business entities. However, thedata is skewed due to lack of sufficient positive samples of businessentities in the candidate generation step. Therefore, to address thisproblem, oversampling is performed using Synthetic MinorityOver-sampling Technique (SMOTE) and cleaning is performed using EditedNearest Neighbors (ENN) algorithm. The combined terminology foroversampling is SMOTEENN. Using SMOTEENN, more positive samples areadded to data while removing noise around the positive samples of thebusiness entities.

In one embodiment, the feed-forward neural network includes a networkarchitecture including multiple LSTM layers and dense layers. In oneexample, in the network architecture, three LSTM layers are applied with256, 128, and 64 hidden nodes, respectively, to learn temporal behaviorin the financial data and news items. Further, two dense layers with 100and 50 nodes, respectively, are utilized. The dense layers areconfigured to map a set of output nodes of the LSTM layer to a singlenode. Further, two auxiliary time independent variables are added to theoutput of dense layer. The output of the dense layer is fed to an outputlayer. Each of the LSTM models and dense layers include Rectified Linearactivation Unit (ReLU) as an activation function. The output layerutilizes a sigmoid function to calculate prediction outputs.

In an exemplary embodiment, a loss function (e.g., a binary crossentropy-loss function) is used in training to calculate the deviationbetween predictions and the desired output. An optimization techniquesuch as, gradient descent optimization (e.g., Nadam) is utilized in thetraining to update weights in the dense layers. The goal of theoptimization technique is to minimize the loss function.

The selection module 508 is configured to select those business entitieswhich have growth rates on acquiring which are higher than a firstpre-determined threshold. The selection module 508 is also configured toselect those business entities which have growth rates on gettingacquired which are higher than a second pre-determined threshold. Theselected business entities are candidate set of business entities whichare used to create dynamic bipartite knowledge graph.

For instance, the server system 200 extracts financial data and newsdata items associated with companies “A”, “B”, “C” and “D”. Based on thefinancial data and news data items, the server system determinesprobable growth rates of getting company A acquired by company B andacquiring company B by company A. In other words, the server system 200attempts to determine growth probabilities values of all possibleacquisition relationships among companies A, B, C and D. Based on thegrowth probability values, the server system 200 makes a decision thatcompanies A, B and C may engage in business acquisition relationship infuture. Therefore, the companies A, B and C are termed as a candidateset of business entities.

Referring now to FIG. 6, a schematic block diagram representation 600depicting conversion process of dynamic bipartite knowledge graph 602into static bipartite knowledge graph 604 is shown, in accordance withone embodiment of the present disclosure.

As mentioned previously, the dynamic bipartite knowledge graph 602 iscreated for each of distinct time durations within a particular timeduration based, at least in part, on company-specific features of thecandidate set of business entities. The dynamic bipartite knowledgegraph includes multiple sub-graphs associated with distinct timedurations.

As shown in the FIG. 6, Q1 graph 602 a represents a sub-graph for firstquarter of a year. Similarly, Q2 graph 602 b, Q3 graph 602 c, and Q4graph 602 d represent the sub-graphs for second, third, and fourthquarters of the year, respectively. The first, second, third and fourthquarters are distinct time durations within the particular time duration(e.g., 1 year). Each node in the sub-graph represents a business entity.Q1 graph 602 a, has four nodes C1, C2, C3, and C4 representing businessentities B1, B2, B3, and B4. Nodes C1, C2, C3, and C4 in Q1 graph 602 arepresent financial data and news data for business entities B1, B2, B3,and B4, respectively, corresponding to first quarter of the year.Similarly, nodes C1, C2, and C3 in Q2 graph 602 b represent financialdata and news data for business entities B1, B2, and B3, respectively,corresponding to second quarter of the year and so on.

Further, link L1 in Q1 graph 602 a represents that business entity B1,corresponding to node C1 has acquired business entity B4, correspondingto node C4 in the first quarter. The strength of the link L1 representsthe probable growth rate of the business entity B1 after acquiringbusiness entity B4. In Q2 graph 602 b, C4 is not present because of B4being acquired by B1, and further link L2 represents that businessentity B3, corresponding to node C3 has acquired business entity B2,corresponding to node C2 in the second quarter. In Q3 graph 602 c, nodesC2 and C4 are not present. In the similar way, link L3 in Q3 graph 602 crepresents that business entity B1, corresponding to node C1 hasacquired business entity B3, corresponding to node C3 in the thirdquarter. Since by the fourth quarter, only business entity B1 is left,as all other business entities were acquired, therefore Q3 graph 602 conly includes node C1 representing financial data and news dataassociated with business entity B1.

The dynamic bipartite knowledge graph 602 keeps on changing with timedue to its dynamic nature. In other words, the attributes or financialdata and news data keep on changing even for every quarter. Further,after every quarter the count of business entities will change if somebusiness entity gets acquired in the previous quarter or if any businessentity shuts down due to some reason. This makes it impossible to applygraph algorithms on the dynamic bipartite knowledge graph 602 due to itsdynamic nature. Therefore, to address this problem, the dynamicbipartite knowledge graph 602 is converted into the static bipartiteknowledge graph 604 using graph conversion algorithms.

In contrast to the dynamic bipartite knowledge graph 602, the staticbipartite knowledge graph 604 does not include sub-graphs representingeach quarter. Instead, each node in the static bipartite knowledge graph604 represents a business entity for a quarter. Therefore, unless abusiness entity is acquired by another business entity, each businessentity will be represented by four nodes corresponding to every quarterof a year. For example, the static bipartite knowledge graph 604includes four nodes C1Q1, C1Q2, C1Q3, and C1Q4 corresponding to businessentity B1 for first, second, third, and fourth quarters of the year,respectively. The edges or links of the static bipartite knowledge graph604 represent which business entity has acquired another business entityin a particular quarter. The strength of the link represents theprobable growth rates associated with business entity determined by thecandidate generation engine 218. In the first quarter, business entityB1 acquired business entity B4. The acquisition is shown by a link L4which is directed from node C1Q1 to node C4Q4. Similarly, links L5 andL6 represent the acquisition of business entities B2 and B3 in secondand third quarters, respectively.

Each node in the static bipartite knowledge graph 604 is characterizedby the financial data and news data for a particular quarter. Therefore,the information represented by each node is constant and does not changewith time in the static bipartite knowledge graph 604.

FIG. 7 represents a flow diagram of a computer-implemented method 700for predicting an acquisition of a business entity by another, inaccordance with an example embodiment. The method 700 depicted in theflow diagram may be executed by the server system 104 or the serversystem 200. Operations of the method 700, and combinations of operationin the method 700, may be implemented by, for example, hardware,firmware, a processor, circuitry and/or a different device associatedwith the execution of software that includes one or more computerprogram instructions. The method 700 starts at operation 702.

As shown in the FIG. 7, at the operation 702, the method 700 includesaccessing financial data items and news items associated with financesof a plurality of business entities from one or more data sources for aparticular time duration.

At operation 704, the method 700 includes generating a plurality offinancial feature vectors and a plurality of news feature vectorscorresponding to the plurality of business entities based, at least inpart, on the financial data items and news items associated withfinances.

At operation 706, the method 700 includes applying machine learningmodels over the plurality of financial feature vectors and the pluralityof news feature vectors associated with the plurality of businessentities for determining a candidate set of business entities predictedto be engaged in business acquisition in future.

At operation 708, the method 700 includes creating a dynamic bipartiteknowledge graph for each of distinct time durations (e.g., quarter of ayear) within the particular time duration (e.g., 2 years) based, atleast in part, on company-specific features of the candidate set ofbusiness entities. The dynamic bipartite knowledge graph represents acomputer-based graph representation of the candidate set of businessentities as nodes, and relationship between the nodes as edges.

At operation 710, the method 700 includes generating a static bipartiteknowledge graph based, at least in part, on dynamic bipartite knowledgegraphs created for the distinct time durations. Nodes of the staticbipartite knowledge graph represent the candidate set of businessentities and a pre-determined time duration (e.g., a particular quarter)and edges of the static bipartite knowledge graph representingrelationship between the nodes for the pre-determined time duration. Thecompany-specific features include the financial data items, the newsitems, and probable growth rates associated with each business entityafter acquisition.

At operation 712, the method 700 includes predicting an occurrence of anacquisition of at least one business entity of the candidate set ofbusiness entities based, at least in part, on a supervised machinelearning model and the static bipartite knowledge graph.

The disclosed method with reference to FIG. 7, or one or more operationsof the server system 200 may be implemented using software includingcomputer-executable instructions stored on one or more computer-readablemedia (e.g., non-transitory computer-readable media, such as one or moreoptical media discs, volatile memory components (e.g., DRAM or SRAM), ornonvolatile memory or storage components (e.g., hard drives orsolid-state nonvolatile memory components, such as Flash memorycomponents) executed on a computer (e.g., any suitable computer, such asa laptop computer, net book, Web book, tablet computing device, smartphone, or other mobile computing device)). Such software may beexecuted, for example, on a single local computer or in a networkenvironment (e.g., via the Internet, a wide-area network, a local-areanetwork, a remote web-based server, a client-server network (such as acloud computing network), or other such network) using one or morenetwork computers. Additionally, any of the intermediate or final datacreated and used during implementation of the disclosed methods orsystems may also be stored on one or more computer-readable media (e.g.,non-transitory computer-readable media) and are considered to be withinthe scope of the disclosed technology. Furthermore, any of thesoftware-based embodiments may be uploaded, downloaded, or remotelyaccessed through a suitable communication means. Such suitablecommunication means include, for example, the Internet, the World WideWeb, an intranet, software applications, cable (including fiber opticcable), magnetic communications, electromagnetic communications(including RF, microwave, and infrared communications), electroniccommunications, or other such communication means.

Although the invention has been described with reference to specificexemplary embodiments, it is noted that various modifications andchanges may be made to these embodiments without departing from thebroad spirit and scope of the invention. For example, the variousoperations, blocks, etc., described herein may be enabled and operatedusing hardware circuitry (for example, complementary metal oxidesemiconductor (CMOS) based logic circuitry), firmware, software and/orany combination of hardware, firmware, and/or software (for example,embodied in a machine-readable medium). For example, the apparatuses andmethods may be embodied using transistors, logic gates, and electricalcircuits (for example, application specific integrated circuit (ASIC)circuitry and/or in Digital Signal Processor (DSP) circuitry).

Particularly, the server system 200 and its various components may beenabled using software and/or using transistors, logic gates, andelectrical circuits (for example, integrated circuit circuitry such asASIC circuitry). Various embodiments of the invention may include one ormore computer programs stored or otherwise embodied on acomputer-readable medium, wherein the computer programs are configuredto cause a processor or computer to perform one or more operations. Acomputer-readable medium storing, embodying, or encoded with a computerprogram, or similar language, may be embodied as a tangible data storagedevice storing one or more software programs that are configured tocause a processor or computer to perform one or more operations. Suchoperations may be, for example, any of the steps or operations describedherein. In some embodiments, the computer programs may be stored andprovided to a computer using any type of non-transitory computerreadable media. Non-transitory computer readable media include any typeof tangible storage media. Examples of non-transitory computer readablemedia include magnetic storage media (such as floppy disks, magnetictapes, hard disk drives, etc.), optical magnetic storage media (e.g.magneto-optical disks), CD-ROM (compact disc read only memory), CD-R(compact disc recordable), CD-R/W (compact disc rewritable), DVD(Digital Versatile Disc), BD (BLU-RAY® Disc), and semiconductor memories(such as mask ROM, PROM (programmable ROM), EPROM (erasable PROM), flashmemory, RAM (random access memory), etc.). Additionally, a tangible datastorage device may be embodied as one or more volatile memory devices,one or more non-volatile memory devices, and/or a combination of one ormore volatile memory devices and non-volatile memory devices. In someembodiments, the computer programs may be provided to a computer usingany type of transitory computer readable media. Examples of transitorycomputer readable media include electric signals, optical signals, andelectromagnetic waves. Transitory computer readable media can providethe program to a computer via a wired communication line (e.g., electricwires, and optical fibers) or a wireless communication line.

Various embodiments of the invention, as discussed above, may bepracticed with steps and/or operations in a different order, and/or withhardware elements in configurations, which are different than thosewhich are disclosed. Therefore, although the invention has beendescribed based upon these exemplary embodiments, it is noted thatcertain modifications, variations, and alternative constructions may beapparent and well within the spirit and scope of the invention.

Although various exemplary embodiments of the invention are describedherein in a language specific to structural features and/ormethodological acts, the subject matter defined in the appended claimsis not necessarily limited to the specific features or acts describedabove. Rather, the specific features and acts described above aredisclosed as exemplary forms of implementing the claims.

What is claimed is:
 1. A computer-implemented method, performed by aserver system, comprising: accessing financial data items and news itemsassociated with finances of a plurality of business entities from one ormore data sources for a particular time duration; generating a pluralityof financial feature vectors and a plurality of news feature vectorscorresponding to the plurality of business entities based, at least inpart, on the financial data items and the news items associated with thefinances; applying machine learning models over the plurality offinancial feature vectors and the plurality of news feature vectorsassociated with the plurality of business entities for determining acandidate set of business entities predicted to be engaged in businessacquisition in future; creating a dynamic bipartite knowledge graph foreach of distinct time durations within the particular time durationbased, at least in part, on company-specific features of the candidateset of business entities, the dynamic bipartite knowledge graphrepresenting a computer-based graph representation of the candidate setof business entities as nodes, and relationship between the nodes asedges; generating a static bipartite knowledge graph based, at least inpart, on dynamic bipartite knowledge graphs created for the distincttime durations, nodes of the static bipartite knowledge graphrepresenting the candidate set of business entities and a pre-determinedtime duration and edges of the static bipartite knowledge graphrepresenting relationship between the nodes for the pre-determined timeduration; and predicting an occurrence of an acquisition of at least onebusiness entity of the candidate set of business entities based, atleast in part, on a supervised machine learning model and the staticbipartite knowledge graph.
 2. The computer-implemented method as claimedin claim 1, wherein applying the machine learning models over theplurality of financial feature vectors and the plurality of news featurevectors associated with the plurality of business entities comprises:applying a first long short-term memory (LSTM) model over the pluralityof financial feature vectors to output a first set of vectors, the firstLSTM model trained based, at least in part, on past financial data itemsassociated with the plurality of business entities; applying a secondlong short-term memory (LSTM) model over the plurality of news featurevectors to output a second set of vectors, the second long short-termmemory model trained based, at least in part, on the news itemsassociated with the plurality of business entities; concatenating thefirst set of vectors and the second set of vectors to obtain a set ofconcatenated vectors; for each business entity of the plurality ofbusiness entities, generating probable growth rates on acquiring otherbusiness entity and on getting acquired by other business entity byapplying a feed-forward neural network model over the set ofconcatenated vectors; and selecting the candidate set of businessentities predicted to be engaged in the business acquisition in thefuture based, at least in part, on the generated probable growth rates.3. The computer-implemented method as claimed in claim 2, whereinstrength of each edge in the dynamic bipartite knowledge graph isdetermined based, at least in part, on the generated probable growthrates.
 4. The computer-implemented method as claimed in claim 2, whereinthe company-specific features comprise the financial data items, thenews items, and the generated probable growth rates associated with theeach business entity.
 5. The computer-implemented method as claimed inclaim 1, wherein predicting the occurrence comprises: encoding thestatic bipartite knowledge graph into graph embedding vectors based, atleast in part, on a bipartite graph embedding model; and applying thesupervised machine learning model over the graph embedding vectors topredict the occurrence.
 6. The computer-implemented method as claimed inclaim 5, wherein encoding the static bipartite knowledge graph into thegraph embedding vectors further comprises: converting the staticbipartite knowledge graph into positive and negative triplets, whereinthe positive triplets are N_(p1) representing a first business entity,N_(p2) representing a second business entity, and E_(p1) representing analready existing link between the first and second business entities,and wherein the negative triplets are N_(n1) representing a firstbusiness entity, N_(n2) representing a second business entity, andE_(n1) representing no link present between the first and secondbusiness entities.
 7. The computer-implemented method as claimed inclaim 1, wherein each financial feature vector is a function of one ormore of: total assets, asset growth rate, total loans, total losses,loan growth rate, gross loans, gross assets, and total deposits.
 8. Aserver system, comprising: a communication interface; a memorycomprising executable instructions; and a processor communicably coupledto the communication interface, the processor configured to execute theexecutable instructions to cause the server system to at least: accessfinancial data items and news items associated with finances of aplurality of business entities from one or more data sources for aparticular time duration; generate a plurality of financial featurevectors and a plurality of news feature vectors corresponding to theplurality of business entities based, at least in part, on the financialdata items and the news items associated with the finances; applymachine learning models over the plurality of financial feature vectorsand the plurality of news feature vectors associated with the pluralityof business entities for determining a candidate set of businessentities predicted to be engaged in business acquisition in future;create a dynamic bipartite knowledge graph for each of distinct timedurations within the particular time duration based, at least in part,on company-specific features of the candidate set of business entities,the dynamic bipartite knowledge graph representing a computer-basedgraph representation of the candidate set of business entities as nodes,and relationship between the nodes as edges; generate a static bipartiteknowledge graph based, at least in part, on dynamic bipartite knowledgegraphs created for the distinct time durations, nodes of the staticbipartite knowledge graph representing the candidate set of businessentities and a pre-determined time duration and edges of the staticbipartite knowledge graph representing relationship between the nodesfor the pre-determined time duration; and predict an occurrence of anacquisition of at least one business entity of the candidate set ofbusiness entities based, at least in part, on a supervised machinelearning model and the static bipartite knowledge graph.
 9. The serversystem as claimed in claim 8, wherein for applying the machine learningmodels over the plurality of financial feature vectors and the pluralityof news feature vectors associated with the plurality of businessentities, the server system is further caused at least in part to: applya first long short-term memory (LSTM) model over the plurality offinancial feature vectors to output a first set of vectors, the firstLSTM model trained based, at least in part, on the financial data itemsassociated with the plurality of business entities; apply a second LSTMmodel over the plurality of news feature vectors to output a second setof vectors; the second LSTM model trained based, at least in part, onthe news items associated with the plurality of business entities;concatenate the first set of vectors and the second set of vectors toobtain a set of concatenated vectors; for each business entity of theplurality of business entities, generate probable growth rates onacquiring other business entity and on getting acquired by otherbusiness entity by applying a feed-forward neural network model over theset of concatenated vectors; and select the candidate set of businessentities predicted to be engaged in the business acquisition in thefuture based, at least in part, on the generated probable growth rates.10. The server system as claimed in claim 9, wherein strength of eachedge in the dynamic bipartite knowledge graph is determined based, atleast in part, on the generated probable growth rates.
 11. The serversystem as claimed in claim 9, wherein the company-specific featurescomprise the financial data items, the news items, and the generatedprobable growth rates associated with each business entity.
 12. Theserver system as claimed in claim 8, wherein the server system isfurther caused at least in part to: encode the static bipartiteknowledge graph into graph embedding vectors based, at least in part, ona bipartite graph embedding model; and apply the supervised machinelearning model over the graph embedding vectors to predict theoccurrence.
 13. The server system as claimed in claim 12, wherein inorder to encode the static bipartite knowledge graph into the graphembedding vectors, the server system is further caused to at least inpart to: convert the static bipartite knowledge graph into positive andnegative triplets, wherein the positive triplets are N_(p1) representinga first business entity, N_(p2) representing a second business entity,and E_(p1) representing an already existing link between the first andsecond business entities, and wherein the negative triplets are N_(n1)representing a first business entity, N_(n2) representing a secondbusiness entity, and E_(n1) representing no link present between thefirst and second business entities.
 14. The server system as claimed inclaim 8, wherein each financial feature vector is a function of one ormore of: total assets, asset growth rate, total loans, total losses,loan growth rate, gross loans, gross assets, and total deposits.
 15. Acomputer-implemented method, performed by a server system, comprising:accessing financial data items and news items associated with financesof a plurality of business entities from one or more data sources for aparticular time duration; generating a plurality of financial featurevectors and a plurality of news feature vectors corresponding to theplurality of business entities based, at least in part, on the financialdata items and the news items associated with the finances; applyingmachine learning models over the plurality of financial feature vectorsand the plurality of news feature vectors associated with the pluralityof business entities for determining a candidate set of businessentities predicted to be engaged in business acquisition in future;creating a dynamic bipartite knowledge graph for each of distinct timedurations within the particular time duration based, at least in part,on company-specific features of the candidate set of business entities,the dynamic bipartite knowledge graph representing a computer-basedgraph representation of the candidate set of business entities as nodes,and relationship between the nodes as edges; generating a staticbipartite knowledge graph based, at least in part, on dynamic bipartiteknowledge graphs created for the distinct time durations, nodes of thestatic bipartite knowledge graph representing the candidate set ofbusiness entities and a pre-determined time duration and edges of thestatic bipartite knowledge graph representing relationship between thenodes for the pre-determined time duration; encoding the staticbipartite knowledge graph into graph embedding vectors based, at leastin part, on a bipartite graph embedding model; and predicting anoccurrence of an acquisition of at least one business entity of thecandidate set of business entities by applying a supervised machinelearning model over the graph embedding vectors.
 16. Thecomputer-implemented method as claimed in claim 15, wherein applying themachine learning models over the plurality of financial feature vectorsand the plurality of news feature vectors associated with the pluralityof business entities comprises: applying a first long short-term memory(LSTM) model using the plurality of financial feature vectors to outputa first set of vectors, the first LSTM model trained based, at least inpart, on the financial data items associated with the plurality ofbusiness entities; applying a LSTM model using the plurality of newsfeature vectors to output a second set of vectors; the second LSTM modeltrained based, at least in part, on the news items associated with theplurality of business entities; concatenating the first set of vectorsand the second set of vectors to obtain a set of concatenated vectors;for each business entity of the plurality of business entities,generating probable growth rates on acquiring other business entity andon getting acquired by other business entity by applying a feed-forwardneural network model over the set of concatenated vectors; and selectingthe candidate set of business entities predicted to be engaged in thebusiness acquisition in the future based, at least in part, on thegenerated probable growth rates.
 17. The computer-implemented method asclaimed in claim 16, wherein strength of each edge in the dynamicbipartite knowledge graph is determined based, at least in part, on thegenerated probable growth rates.
 18. The computer-implemented method asclaimed in claim 15, wherein the company-specific features comprise thefinancial data items, the news items, and the generated probable growthrates associated with each business entity.